Overview

Dataset statistics

Number of variables34
Number of observations464
Missing cells25
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory123.4 KiB
Average record size in memory272.3 B

Variable types

CAT18
NUM16

Warnings

TempIL is highly correlated with TempGLHigh correlation
TempGL is highly correlated with TempILHigh correlation
SpatIL is highly correlated with SpatGLHigh correlation
SpatGL is highly correlated with SpatILHigh correlation
Lich2 is highly correlated with Lich1High correlation
Lich1 is highly correlated with Lich2High correlation
TempIL is highly correlated with TempGLHigh correlation
TempGL is highly correlated with TempILHigh correlation
Fstf has 25 (5.4%) missing values Missing
df_index has unique values Unique
TempDist has 391 (84.3%) zeros Zeros
SpatDist has 371 (80.0%) zeros Zeros
UArt1 has 13 (2.8%) zeros Zeros
AUrs1 has 430 (92.7%) zeros Zeros

Reproduction

Analysis started2020-11-13 16:36:45.735012
Analysis finished2020-11-13 16:38:08.080129
Duration1 minute and 22.35 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct464
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean977.6530172
Minimum2
Maximum1852
Zeros0
Zeros (%)0.0%
Memory size3.6 KiB
2020-11-13T17:38:08.434576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile125.35
Q1533.75
median999.5
Q31417.25
95-th percentile1754.7
Maximum1852
Range1850
Interquartile range (IQR)883.5

Descriptive statistics

Standard deviation523.9403976
Coefficient of variation (CV)0.5359165147
Kurtosis-1.122824791
Mean977.6530172
Median Absolute Deviation (MAD)435
Skewness-0.133438332
Sum453631
Variance274513.5403
MonotocityStrictly increasing
2020-11-13T17:38:08.594018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
102310.2%
 
136410.2%
 
32110.2%
 
32210.2%
 
134910.2%
 
32810.2%
 
33010.2%
 
33110.2%
 
135610.2%
 
135810.2%
 
Other values (454)45497.8%
 
ValueCountFrequency (%) 
210.2%
 
510.2%
 
810.2%
 
910.2%
 
1010.2%
 
ValueCountFrequency (%) 
185210.2%
 
183710.2%
 
183510.2%
 
183410.2%
 
183310.2%
 

TempMax
Real number (ℝ≥0)

Distinct166
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214.6293103
Minimum9
Maximum1323
Zeros0
Zeros (%)0.0%
Memory size3.6 KiB
2020-11-13T17:38:08.746490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile24
Q178
median147
Q3282
95-th percentile615
Maximum1323
Range1314
Interquartile range (IQR)204

Descriptive statistics

Standard deviation206.9983162
Coefficient of variation (CV)0.9644457033
Kurtosis7.430702116
Mean214.6293103
Median Absolute Deviation (MAD)87
Skewness2.33150815
Sum99588
Variance42848.3029
MonotocityNot monotonic
2020-11-13T17:38:08.897296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
129112.4%
 
7891.9%
 
8191.9%
 
3091.9%
 
13581.7%
 
11771.5%
 
7571.5%
 
8771.5%
 
4271.5%
 
12371.5%
 
Other values (156)38382.5%
 
ValueCountFrequency (%) 
930.6%
 
1230.6%
 
1520.4%
 
1861.3%
 
2151.1%
 
ValueCountFrequency (%) 
132320.4%
 
125720.4%
 
119410.2%
 
102910.2%
 
96010.2%
 

TempAvg
Real number (ℝ≥0)

Distinct171
Distinct (%)36.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.75215517
Minimum4
Maximum1326
Zeros0
Zeros (%)0.0%
Memory size3.6 KiB
2020-11-13T17:38:09.037756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile10
Q129
median60
Q399
95-th percentile227.55
Maximum1326
Range1322
Interquartile range (IQR)70

Descriptive statistics

Standard deviation115.8272015
Coefficient of variation (CV)1.350720588
Kurtosis58.74292713
Mean85.75215517
Median Absolute Deviation (MAD)34
Skewness6.53417767
Sum39789
Variance13415.9406
MonotocityNot monotonic
2020-11-13T17:38:09.193096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1871.5%
 
1171.5%
 
9971.5%
 
4471.5%
 
2671.5%
 
5671.5%
 
2471.5%
 
2371.5%
 
8971.5%
 
871.5%
 
Other values (161)39484.9%
 
ValueCountFrequency (%) 
410.2%
 
510.2%
 
630.6%
 
751.1%
 
871.5%
 
ValueCountFrequency (%) 
132610.2%
 
126010.2%
 
95510.2%
 
70310.2%
 
57510.2%
 

SpatMax
Real number (ℝ≥0)

Distinct422
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12432.21767
Minimum832
Maximum48278
Zeros0
Zeros (%)0.0%
Memory size3.6 KiB
2020-11-13T17:38:09.339262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum832
5-th percentile2154.45
Q15489.75
median9553.5
Q316986
95-th percentile35366.75
Maximum48278
Range47446
Interquartile range (IQR)11496.25

Descriptive statistics

Standard deviation9743.279808
Coefficient of variation (CV)0.7837121312
Kurtosis1.784032433
Mean12432.21767
Median Absolute Deviation (MAD)4981.5
Skewness1.446928074
Sum5768549
Variance94931501.42
MonotocityNot monotonic
2020-11-13T17:38:09.487590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
602530.6%
 
957430.6%
 
991730.6%
 
662130.6%
 
4239330.6%
 
1196220.4%
 
4151320.4%
 
2667820.4%
 
347520.4%
 
4003320.4%
 
Other values (412)43994.6%
 
ValueCountFrequency (%) 
83210.2%
 
111010.2%
 
119410.2%
 
120610.2%
 
128010.2%
 
ValueCountFrequency (%) 
4827810.2%
 
4632810.2%
 
4513010.2%
 
4273610.2%
 
4265810.2%
 

SpatAvg
Real number (ℝ≥0)

Distinct431
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3952.114224
Minimum583
Maximum13744
Zeros0
Zeros (%)0.0%
Memory size3.6 KiB
2020-11-13T17:38:09.628296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum583
5-th percentile1033.45
Q12036
median3334.5
Q35565.25
95-th percentile8672.05
Maximum13744
Range13161
Interquartile range (IQR)3529.25

Descriptive statistics

Standard deviation2474.113542
Coefficient of variation (CV)0.626022782
Kurtosis0.8341885107
Mean3952.114224
Median Absolute Deviation (MAD)1602.5
Skewness1.021714383
Sum1833781
Variance6121237.816
MonotocityNot monotonic
2020-11-13T17:38:09.780860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
402230.6%
 
242230.6%
 
560630.6%
 
390720.4%
 
828520.4%
 
156420.4%
 
297820.4%
 
623520.4%
 
728620.4%
 
264420.4%
 
Other values (421)44195.0%
 
ValueCountFrequency (%) 
58310.2%
 
62510.2%
 
64310.2%
 
66010.2%
 
67010.2%
 
ValueCountFrequency (%) 
1374410.2%
 
1248010.2%
 
1236310.2%
 
1228810.2%
 
1217410.2%
 

TempDist
Real number (ℝ≥0)

ZEROS

Distinct25
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.064655172
Minimum0
Maximum24
Zeros391
Zeros (%)84.3%
Memory size3.6 KiB
2020-11-13T17:38:09.915683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile17
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.47210881
Coefficient of variation (CV)2.650374204
Kurtosis6.158966404
Mean2.064655172
Median Absolute Deviation (MAD)0
Skewness2.697013421
Sum958
Variance29.94397483
MonotocityNot monotonic
2020-11-13T17:38:10.039008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
039184.3%
 
1871.5%
 
561.3%
 
661.3%
 
1761.3%
 
2451.1%
 
1251.1%
 
940.9%
 
1630.6%
 
230.6%
 
Other values (15)286.0%
 
ValueCountFrequency (%) 
039184.3%
 
110.2%
 
230.6%
 
310.2%
 
410.2%
 
ValueCountFrequency (%) 
2451.1%
 
2330.6%
 
2220.4%
 
2130.6%
 
2010.2%
 

SpatDist
Real number (ℝ≥0)

ZEROS

Distinct80
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.3103448
Minimum0
Maximum2000
Zeros371
Zeros (%)80.0%
Memory size3.6 KiB
2020-11-13T17:38:10.189016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile724.8
Maximum2000
Range2000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation308.5025048
Coefficient of variation (CV)2.874862674
Kurtosis14.60643133
Mean107.3103448
Median Absolute Deviation (MAD)0
Skewness3.704535454
Sum49792
Variance95173.79549
MonotocityNot monotonic
2020-11-13T17:38:10.470317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
037180.0%
 
25040.9%
 
29030.6%
 
125030.6%
 
5030.6%
 
71820.4%
 
11220.4%
 
25220.4%
 
320.4%
 
21020.4%
 
Other values (70)7015.1%
 
ValueCountFrequency (%) 
037180.0%
 
320.4%
 
2110.2%
 
2210.2%
 
5030.6%
 
ValueCountFrequency (%) 
200010.2%
 
194910.2%
 
176610.2%
 
171310.2%
 
170310.2%
 

Coverage
Real number (ℝ≥0)

Distinct75
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.48491379
Minimum6
Maximum100
Zeros0
Zeros (%)0.0%
Memory size3.6 KiB
2020-11-13T17:38:10.625106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11.15
Q124
median35.5
Q348
95-th percentile70
Maximum100
Range94
Interquartile range (IQR)24

Descriptive statistics

Standard deviation18.03056527
Coefficient of variation (CV)0.4810085829
Kurtosis0.2598351192
Mean37.48491379
Median Absolute Deviation (MAD)11.5
Skewness0.6757821009
Sum17393
Variance325.1012838
MonotocityNot monotonic
2020-11-13T17:38:10.762309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
40173.7%
 
30153.2%
 
18143.0%
 
37143.0%
 
42132.8%
 
25132.8%
 
35132.8%
 
38122.6%
 
31122.6%
 
34112.4%
 
Other values (65)33071.1%
 
ValueCountFrequency (%) 
640.9%
 
720.4%
 
820.4%
 
910.2%
 
1081.7%
 
ValueCountFrequency (%) 
10020.4%
 
9010.2%
 
8840.9%
 
8610.2%
 
8510.2%
 

TempGL
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
3
391 
5
73 
ValueCountFrequency (%) 
339184.3%
 
57315.7%
 
2020-11-13T17:38:11.141223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:38:11.612314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:19.831565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

SpatGL
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2
371 
3
93 
ValueCountFrequency (%) 
237180.0%
 
39320.0%
 
2020-11-13T17:38:19.936390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:38:20.012822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:30.753462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

TempIL
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
4
215 
5
176 
-1
73 
ValueCountFrequency (%) 
421546.3%
 
517637.9%
 
-17315.7%
 
2020-11-13T17:38:30.874703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:38:30.970724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:42.402626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.157327586
Min length1

SpatIL
Real number (ℝ)

HIGH CORRELATION

Distinct6
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.140086207
Minimum-1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size3.6 KiB
2020-11-13T17:38:42.507438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q12.75
median4
Q35
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation2.19458119
Coefficient of variation (CV)0.6988920195
Kurtosis-0.277976104
Mean3.140086207
Median Absolute Deviation (MAD)1
Skewness-1.17050828
Sum1457
Variance4.816186602
MonotocityNot monotonic
2020-11-13T17:38:42.606170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
420744.6%
 
512827.6%
 
-19320.0%
 
2204.3%
 
3132.8%
 
130.6%
 
ValueCountFrequency (%) 
-19320.0%
 
130.6%
 
2204.3%
 
3132.8%
 
420744.6%
 
ValueCountFrequency (%) 
512827.6%
 
420744.6%
 
3132.8%
 
2204.3%
 
130.6%
 

TLCar
Real number (ℝ≥0)

Distinct355
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1502.331897
Minimum1001
Maximum1996
Zeros0
Zeros (%)0.0%
Memory size3.6 KiB
2020-11-13T17:38:42.736635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1039.75
Q11254.75
median1512.5
Q31750.5
95-th percentile1945.85
Maximum1996
Range995
Interquartile range (IQR)495.75

Descriptive statistics

Standard deviation287.8455118
Coefficient of variation (CV)0.1915991482
Kurtosis-1.200281265
Mean1502.331897
Median Absolute Deviation (MAD)249.5
Skewness0.004077355296
Sum697082
Variance82855.03863
MonotocityNot monotonic
2020-11-13T17:38:42.888990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
188140.9%
 
117940.9%
 
145140.9%
 
155240.9%
 
140330.6%
 
135930.6%
 
191030.6%
 
129530.6%
 
155730.6%
 
121530.6%
 
Other values (345)43092.7%
 
ValueCountFrequency (%) 
100110.2%
 
100320.4%
 
100610.2%
 
101410.2%
 
101510.2%
 
ValueCountFrequency (%) 
199610.2%
 
199210.2%
 
199110.2%
 
198810.2%
 
198510.2%
 

TLHGV
Real number (ℝ≥0)

Distinct292
Distinct (%)62.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean748.5969828
Minimum501
Maximum998
Zeros0
Zeros (%)0.0%
Memory size3.6 KiB
2020-11-13T17:38:43.041491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum501
5-th percentile531.15
Q1627.5
median752.5
Q3869.25
95-th percentile966.55
Maximum998
Range497
Interquartile range (IQR)241.75

Descriptive statistics

Standard deviation140.9185666
Coefficient of variation (CV)0.1882435674
Kurtosis-1.211408931
Mean748.5969828
Median Absolute Deviation (MAD)122.5
Skewness0.005083237898
Sum347349
Variance19858.04241
MonotocityNot monotonic
2020-11-13T17:38:43.206644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
52251.1%
 
53451.1%
 
62640.9%
 
70240.9%
 
63140.9%
 
59840.9%
 
63940.9%
 
85740.9%
 
85540.9%
 
73340.9%
 
Other values (282)42290.9%
 
ValueCountFrequency (%) 
50120.4%
 
50610.2%
 
50710.2%
 
51110.2%
 
51310.2%
 
ValueCountFrequency (%) 
99810.2%
 
99710.2%
 
99110.2%
 
99010.2%
 
98930.6%
 

Strasse
Categorical

Distinct12
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
A3
161 
A9
134 
A99
34 
A73
29 
A96
25 
Other values (7)
81 
ValueCountFrequency (%) 
A316134.7%
 
A913428.9%
 
A99347.3%
 
A73296.2%
 
A96255.4%
 
A6245.2%
 
A7183.9%
 
A92153.2%
 
A94112.4%
 
A93102.2%
 
Other values (2)30.6%
 
2020-11-13T17:38:43.366343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.2%
2020-11-13T17:38:43.503535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.273706897
Min length2

Kat
Categorical

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
7
234 
3
206 
2
 
21
1
 
3
ValueCountFrequency (%) 
723450.4%
 
320644.4%
 
2214.5%
 
130.6%
 
2020-11-13T17:38:43.624797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:38:43.704883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:54.754979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Typ
Real number (ℝ≥0)

Distinct5
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.181034483
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size3.6 KiB
2020-11-13T17:38:54.980013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median6
Q36
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.761597243
Coefficient of variation (CV)0.3400087856
Kurtosis1.077549133
Mean5.181034483
Median Absolute Deviation (MAD)0
Skewness-1.639225801
Sum2404
Variance3.103224845
MonotocityNot monotonic
2020-11-13T17:38:55.074259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
634875.0%
 
15511.9%
 
3398.4%
 
7173.7%
 
551.1%
 
ValueCountFrequency (%) 
15511.9%
 
3398.4%
 
551.1%
 
634875.0%
 
7173.7%
 
ValueCountFrequency (%) 
7173.7%
 
634875.0%
 
551.1%
 
3398.4%
 
15511.9%
 

Betei
Real number (ℝ≥0)

Distinct8
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.226293103
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size3.6 KiB
2020-11-13T17:38:55.180032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile3.85
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7958304194
Coefficient of variation (CV)0.3574688428
Kurtosis15.02266132
Mean2.226293103
Median Absolute Deviation (MAD)0
Skewness2.748069275
Sum1033
Variance0.6333460565
MonotocityNot monotonic
2020-11-13T17:38:55.276276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
232169.2%
 
38117.5%
 
1388.2%
 
4194.1%
 
820.4%
 
710.2%
 
610.2%
 
510.2%
 
ValueCountFrequency (%) 
1388.2%
 
232169.2%
 
38117.5%
 
4194.1%
 
510.2%
 
ValueCountFrequency (%) 
820.4%
 
710.2%
 
610.2%
 
510.2%
 
4194.1%
 

UArt1
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.120689655
Minimum0
Maximum9
Zeros13
Zeros (%)2.8%
Memory size3.6 KiB
2020-11-13T17:38:55.389081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.092484432
Coefficient of variation (CV)0.6705198734
Kurtosis1.889846307
Mean3.120689655
Median Absolute Deviation (MAD)1
Skewness1.630609383
Sum1448
Variance4.3784911
MonotocityNot monotonic
2020-11-13T17:38:55.485218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
220544.2%
 
313930.0%
 
5316.7%
 
8275.8%
 
1224.7%
 
9204.3%
 
0132.8%
 
761.3%
 
610.2%
 
ValueCountFrequency (%) 
0132.8%
 
1224.7%
 
220544.2%
 
313930.0%
 
5316.7%
 
ValueCountFrequency (%) 
9204.3%
 
8275.8%
 
761.3%
 
610.2%
 
5316.7%
 

UArt2
Real number (ℝ)

Distinct7
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.3512931034
Minimum-1
Maximum9
Zeros1
Zeros (%)0.2%
Memory size3.6 KiB
2020-11-13T17:38:55.588793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile8
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.305847213
Coefficient of variation (CV)-6.563884091
Kurtosis10.85697014
Mean-0.3512931034
Median Absolute Deviation (MAD)0
Skewness3.516586512
Sum-163
Variance5.31693137
MonotocityNot monotonic
2020-11-13T17:38:55.688833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
-142591.6%
 
9183.9%
 
881.7%
 
381.7%
 
520.4%
 
120.4%
 
010.2%
 
ValueCountFrequency (%) 
-142591.6%
 
010.2%
 
120.4%
 
381.7%
 
520.4%
 
ValueCountFrequency (%) 
9183.9%
 
881.7%
 
520.4%
 
381.7%
 
120.4%
 

AUrs1
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.556034483
Minimum0
Maximum89
Zeros430
Zeros (%)92.7%
Memory size3.6 KiB
2020-11-13T17:38:55.789660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile73
Maximum89
Range89
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.83857877
Coefficient of variation (CV)3.570636365
Kurtosis9.178460834
Mean5.556034483
Median Absolute Deviation (MAD)0
Skewness3.321884776
Sum2578
Variance393.5692076
MonotocityNot monotonic
2020-11-13T17:38:55.878238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
043092.7%
 
73163.4%
 
7291.9%
 
8240.9%
 
8920.4%
 
8820.4%
 
8010.2%
 
ValueCountFrequency (%) 
043092.7%
 
7291.9%
 
73163.4%
 
8010.2%
 
8240.9%
 
ValueCountFrequency (%) 
8920.4%
 
8820.4%
 
8240.9%
 
8010.2%
 
73163.4%
 

AUrs2
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0
462 
81
 
1
80
 
1
ValueCountFrequency (%) 
046299.6%
 
8110.2%
 
8010.2%
 
2020-11-13T17:38:55.989072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)0.4%
2020-11-13T17:38:56.067212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:56.159891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.004310345
Min length1

AufHi
Categorical

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
-1
393 
3
57 
4
 
11
5
 
3
ValueCountFrequency (%) 
-139384.7%
 
35712.3%
 
4112.4%
 
530.6%
 
2020-11-13T17:38:56.280100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:38:56.369748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:39:02.025572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.846982759
Min length1

Alkoh
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
-1
458 
1
 
6
ValueCountFrequency (%) 
-145898.7%
 
161.3%
 
2020-11-13T17:39:02.150785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:39:02.227064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:39:05.157978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.987068966
Min length1

Char1
Categorical

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
-1
440 
5
 
11
4
 
10
6
 
3
ValueCountFrequency (%) 
-144094.8%
 
5112.4%
 
4102.2%
 
630.6%
 
2020-11-13T17:39:05.268916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:39:05.348523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:39:08.221706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.948275862
Min length1

Char2
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
-1
458 
6
 
6
ValueCountFrequency (%) 
-145898.7%
 
661.3%
 
2020-11-13T17:39:08.337123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:39:08.416520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:39:08.495971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.987068966
Min length1

Lich1
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0
396 
2
49 
1
 
19
ValueCountFrequency (%) 
039685.3%
 
24910.6%
 
1194.1%
 
2020-11-13T17:39:08.618809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:39:08.714499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:39:23.318843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Lich2
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
-1
396 
4
66 
3
 
2
ValueCountFrequency (%) 
-139685.3%
 
46614.2%
 
320.4%
 
2020-11-13T17:39:23.459077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:39:23.557601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:39:23.668221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.853448276
Min length1

Zust1
Categorical

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
0
366 
1
88 
2
 
9
-1
 
1
ValueCountFrequency (%) 
036678.9%
 
18819.0%
 
291.9%
 
-110.2%
 
2020-11-13T17:39:23.942542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.2%
2020-11-13T17:39:24.033427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:39:35.908544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.002155172
Min length1

Zust2
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
-1
460 
2
 
4
ValueCountFrequency (%) 
-146099.1%
 
240.9%
 
2020-11-13T17:39:36.021493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:39:36.099994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:39:39.028808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.99137931
Min length1

Fstf
Categorical

MISSING

Distinct6
Distinct (%)1.4%
Missing25
Missing (%)5.4%
Memory size3.6 KiB
2
215 
1
136 
3
69 
4
 
12
S
 
6
ValueCountFrequency (%) 
221546.3%
 
113629.3%
 
36914.9%
 
4122.6%
 
S61.3%
 
510.2%
 
(Missing)255.4%
 
2020-11-13T17:39:39.147512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.2%
2020-11-13T17:39:39.238986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:39:48.500273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.107758621
Min length1

WoTag
Categorical

Distinct8
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
Fr
93 
Do
84 
Mi
72 
Mo
66 
Di
62 
Other values (3)
87 
ValueCountFrequency (%) 
Fr9320.0%
 
Do8418.1%
 
Mi7215.5%
 
Mo6614.2%
 
Di6213.4%
 
Sa439.3%
 
So418.8%
 
30.6%
 
2020-11-13T17:39:48.648357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:39:48.737488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:40:00.743019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.987068966
Min length0

FeiTag
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
-1
454 
1
 
10
ValueCountFrequency (%) 
-145497.8%
 
1102.2%
 
2020-11-13T17:40:00.874014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:40:00.947790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:40:03.940839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.978448276
Min length1

Month
Categorical

Distinct12
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
Jul
78 
Aug
54 
Sep
49 
Oct
43 
Nov
39 
Other values (7)
201 
ValueCountFrequency (%) 
Jul7816.8%
 
Aug5411.6%
 
Sep4910.6%
 
Oct439.3%
 
Nov398.4%
 
Mar367.8%
 
Apr337.1%
 
Dec337.1%
 
May306.5%
 
Jun296.2%
 
Other values (2)408.6%
 
2020-11-13T17:40:04.077872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-13T17:40:04.217817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Interactions

2020-11-13T17:36:53.407941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:36:55.056758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:36:56.478018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:36:57.738378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:36:58.994690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:00.559548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:01.792015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:03.077202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:04.360497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:05.607438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:07.039763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:08.611874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:10.153913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:11.578435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:12.897065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:14.324666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:16.729239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:16.750313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:16.886353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:17.014810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:17.141058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:17.279151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:17.402345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:17.528947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:17.643952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:18.177149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:18.290038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:18.408897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:18.521924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:18.650973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:18.767839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:18.879466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:20.454593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:20.474530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:20.592777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:20.712128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:20.832965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:20.961316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:21.086031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:21.223631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:21.350789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:21.485866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:21.596098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:21.727640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:21.849251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:21.966375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:22.115019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:22.246733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:23.663127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:23.681397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:23.799148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:23.913237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:24.020207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:24.141849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:24.262772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:24.385200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:24.489588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:24.606159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:24.717415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:24.825170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:24.941096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:25.067159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:25.175805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:25.280483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:26.808098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:26.830118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:26.957963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-11-13T17:37:40.320511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-11-13T17:37:40.913095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:41.030864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:41.152541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:41.256855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:41.369385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:42.755377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:42.773772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:42.888885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:43.008239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:43.115634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:43.236071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-11-13T17:37:43.570319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:43.683082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:43.792014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-11-13T17:37:44.342803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:45.854321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:45.878490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:45.995810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-11-13T17:37:46.353895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-11-13T17:37:47.386966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:47.495172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:48.958900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:48.980706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:49.106047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:49.236966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:49.350149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:49.491279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:49.619860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:49.750788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:49.863022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:49.982967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:50.106106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:50.220021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:50.339281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:50.458279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:50.576771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:50.689178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:52.199959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:52.221101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:52.352292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:52.478804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-11-13T17:37:53.013268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:53.131935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:53.254794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:53.385191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:53.512955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:53.637779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:53.764562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:53.893175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:54.012432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:55.428212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:55.449233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:55.561642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:55.671523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:55.777400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:55.890539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:55.997769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-11-13T17:37:56.229676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:56.334080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:56.446717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:56.555244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:56.665683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:56.773672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:56.871802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:56.978534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:58.472564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:58.492675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:58.605605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:58.713039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:58.816873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:58.935103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:59.036195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:59.166482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:59.274701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:59.382026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:59.504419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:59.635759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:59.752464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:59.868210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:37:59.966449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:00.071745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:01.430756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:01.449379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:01.567142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:01.691366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:01.799618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:01.914384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:02.021386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:02.140391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:02.242602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:02.348069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:02.448578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:02.569616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:02.680939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:02.788325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:02.888087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:02.994207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-13T17:40:05.605906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-13T17:40:06.947036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-13T17:40:08.336193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-13T17:40:09.785610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-11-13T17:40:09.859661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-11-13T17:38:05.060388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:06.699076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-13T17:38:08.040918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexTempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTempGLSpatGLTempILSpatILTLCarTLHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Lich1Lich2Zust1Zust2FstfWoTagFeiTagMonth
0216289139257212005032441293804A336529003-1-1-10-11-12Mi-1Jan
15452074833057004232551044780A63632-100-11-1-10-10-11Mi-1Jan
281385564153142004232441803985A97123-100-1-1-1-1240-11Fr-1Jan
391056199412555011222335-11657905A93632-100-1-1-1-1241-14Fr-1Jan
410873225314134387001332551674752A37119-17303-1-1-10-11-13Sa-1Jan
513189762798710911003932441247531A93123-1720-1-15-10-1121Sa-1Jan
6199744612207904952-141925812A77118-17203-1-1-1242-12So1Jan
727452947392782212106153-1-11015984A67321-100-1-1-1-10-1122Mi-1Jan
831905661443235005132551881598A676208003-1-1-10-11-12Do-1Jan
9331268328962010006532551659895A937623-100-1-1-1-1242-12Do-1Jan

Last rows

df_indexTempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTempGLSpatGLTempILSpatILTLCarTLHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Lich1Lich2Zust1Zust2FstfWoTagFeiTagMonth
4541817781967411718002332441285899A943622-100-1-1-1-1140-13Di-1Dec
455182415037147556121004132551913774A33642-100-1-1-1-1241-11-1Dec
4561827181124171564004832441097663A97118-1003-156240-13Do-1Dec
4571829792263370468388002232441421655A37622-100-1-1-1-1241-12-1Dec
4581830696199239666775002732551854982A33632-100-1-1-1-1241-12Fr-1Dec
4591833729275369118020002132551782567A33632-100-1-1-1-10-10-12Sa-1Dec
4601834300160156398285005232441076855A33681-100-1-1-1-10-10-12Sa-1Dec
4611835300160156398285005232551076855A33622-100-1-16-10-10-12Sa-1Dec
46218378724170693715031021335-11017969A33622-100-1-1-1-10-10-12Sa-1Dec
46318521234685193828004232441928876A93621-100-1-1-1-10-10-11Do1Dec